Abstract

Protein allostery has been well accepted to be an intrinsic feature of all dynamic proteins. A perturbation at one site of the protein could distantly affect another site. The residues involved in these sites are considered as allosteric residues. Here, we argue that all residues in a protein are allosteric residues. We used hybrid models including molecular dynamics simulations and machine learning components to investigate whether a single or multiple properties of protein residues are changed upon ligand binding in PDZ3 domain. Also, we tried to understand whether various residues are affected similarly or in different ways when the ligand is bound. Our deep neural networks and random forests trained with different residue properties of molecular dynamics trajectories revealed that not only many properties of residues are affected upon ligand binding, but also each residue is affected through perturbation of its various properties, which makes the residue distinguishable from other residues. In other words, upon perturbation, different properties of each residue are affected at distinct extents, demonstrating that all residues are allosteric residues. According to our findings in this model protein, we defined a “residue perturbation map” as a two-dimensional map that fingerprints a protein based on the extent of perturbation in different properties of all its residues in a quantitative fashion. This “residue perturbation map” provides a novel way to systematically describe the protein allosteric effects of each residue upon perturbation.

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Research Area

Pharmaceutical Sciences

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Poster

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Probing protein allostery through residue perturbation maps

Protein allostery has been well accepted to be an intrinsic feature of all dynamic proteins. A perturbation at one site of the protein could distantly affect another site. The residues involved in these sites are considered as allosteric residues. Here, we argue that all residues in a protein are allosteric residues. We used hybrid models including molecular dynamics simulations and machine learning components to investigate whether a single or multiple properties of protein residues are changed upon ligand binding in PDZ3 domain. Also, we tried to understand whether various residues are affected similarly or in different ways when the ligand is bound. Our deep neural networks and random forests trained with different residue properties of molecular dynamics trajectories revealed that not only many properties of residues are affected upon ligand binding, but also each residue is affected through perturbation of its various properties, which makes the residue distinguishable from other residues. In other words, upon perturbation, different properties of each residue are affected at distinct extents, demonstrating that all residues are allosteric residues. According to our findings in this model protein, we defined a “residue perturbation map” as a two-dimensional map that fingerprints a protein based on the extent of perturbation in different properties of all its residues in a quantitative fashion. This “residue perturbation map” provides a novel way to systematically describe the protein allosteric effects of each residue upon perturbation.